The Machine Learning Pipeline on AWS
Training at a glance
Level
Intermediate
Duration
4 Days
Experience
1 year Python and Cloud
Average Salary
$187,000
Labs
Yes
Level
Intermediate
Duration
4 Days
Experience
1 year Python and Cloud
Average Salary
$187,000
Labs
Yes
Training Details
This course is designed to teach you how to:
- Select an appropriate ML approach for a business problem
- How to use the ML pipeline to solve a specific business problem
- How to train, evaluate, deploy, and tune an ML model in Amazon SageMaker
- The best practices for designing scalable, cost-optimised, and secure ML pipelines in AWS
- How to apply ML to a real-life problem in your business
- Introduction
- Pre-assessment.
Module 2
- Introduction to Machine Learning and the ML Pipeline
- Overview of machine learning, including use cases, types of machine learning, and key concepts
- Overview of the ML pipeline
- Introduction to course projects and approach
Module 3
- Introduction to Amazon SageMaker
- Introduction to Amazon SageMaker
- Demo and hands- on lab: Amazon SageMaker and Jupyter notebooks
Module 4
- Problem Formulation
- Overview of problem formulation and deciding if ML is the right solution
- Converting a business problem into an ML problem
- Demo and hands- on lab: Amazon SageMaker Ground Truth
- Practice problem formulation
- Formulate problems for projects
Module 5
- Preprocessing
- Overview of data collection and integration, and techniques for data preprocessing and visualisation
- Practice preprocessing
- Preprocess project data and discuss project progress
Module 6
- Model Training
- Choosing the right algorithm
- Formatting and splitting your data for training
- Loss functions and gradient descent for improving your model
- Demo: Create a training job in Amazon SageMaker
Module 7
- Model Evaluation
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models, then present findings
Module 8
- Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimisation
- Practice feature engineering and model tuning
- Apply feature engineering and model tuning to projects
- Final project presentations
Module 9
- Deployment
- How to deploy, inference, and monitor your model on Amazon SageMaker
- Deploying ML at the edge
- Demo: Creating an Amazon SageMaker endpoint
- Post-assessment Course wrap-up
This course is intended for Developers, Solutions Architects, Data Engineers, Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning.
We recommend that attendees of this course have:
- Basic knowledge of Python,
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
We recommend that attendees of this course have:
- Basic knowledge of Python
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Basic understanding of working in a Jupyter notebook environment
Upcoming Classes
We Offer More Than Just AWS Training
Our successful training results keep our corporate and military clients returning. That’s because we provide everything you need to succeed. This is true for all of our courses.
Strategic Planning & Project Management
From Lean Six Sigma to Project Management Institute Project Management Professional, Agile and SCRUM, we offer the best-in-class strategic planning and project management training available. Work closely with our seasoned multi-decade project managers.
IT & Cybersecurity
ATA is the leading OffSec and Hack the Box US training provider, and a CompTIA and EC-Council award-winning training partner. We offer the best offensive and defensive cyber training to keep your team ahead of the technology skills curve.
Leadership & Management
Let us teach your team the high-level traits and micro-level tools & strategies of effective 21st-century leadership. Empower your team to play to each others’ strengths, inspire others and build a culture that values communication, authenticity, and community.
